The field of artificial intelligence (AI) advanced significantly in the previous decade due to developments in deep learning [1]}. In the early years, deep learning methods showed stellar performance for supervised learning where each data sample is coupled with ground truth (labelled data) e.g., each image is associated with a category. Unfortunately generating labelled data sets is time consuming, expensive and there may not exist enough experts for labelling the data at hand (e.g. medical images). The straightforward solution is to use clustering for large-scale problems.
